Ratoon Stunting Disease of Sugarcane: A Review Emphasizing Detection Strategies and Challenges.

Sugarcane (Saccharum hybrid) is an important cash crop grown in tropical and subtropical countries. Ratoon stunting disease (RSD), caused by a xylem-inhabiting bacterium, Leifsonia xyli subsp. xyli (Lxx) is one of the most economically significant diseases globally. RSD results in severe yield losses because its highly contagious nature and lack of visually identifiable symptoms make it harder to devise an effective management strategy. The efficacy of current management practices is hindered by implementation difficulties caused by lack of resources, high cost, and difficulties in monitoring. Rapid detection of the causal pathogen in vegetative planting material is crucial for sugarcane growers to manage this disease. Several microscopic, serological, and molecular-based methods have been developed and used for detecting the RSD pathogen. Although these methods have been used across the sugarcane industry worldwide to diagnose Lxx, some lack reliability or specificity, are expensive and time-consuming to apply, and most of all, are not suitable for on-farm diagnosis. In recent decades, there has been significant progress in the development of integrated isothermal amplification-based microdevices for accurate human and plant pathogen detection. There is a significant opportunity to develop a novel diagnostic method that integrates nanobiosensing with isothermal amplification within a microdevice format for accurate Lxx detection. In this review, we summarize (i) the historical background and current knowledge of sugarcane ratoon stunting disease, including some aspects related to transmission, pathosystem, and management practices; and (ii) the drawbacks of current diagnostic methods and the potential for application of advanced diagnostics to improve disease management.

Corrigendum: Evaluation of lung function in a German single center cohort of young patients with sickle cell disease using EIT and standard techniques.

[This corrects the article DOI: 10.3389/fmed.2023.1100180.].

Evaluation of lung function in a German single center cohort of young patients with sickle cell disease using EIT and standard techniques.

Background and objective: Sickle cell disease (SCD) is a very common autosomal recessive hemoglobinopathy leading to multiple pulmonary complications that are closely associated with mortality. The pathophysiology of chronic pulmonary involvement is not yet fully understood and no specific therapies are available. Methods: The aim of this cross-sectional study was to characterize the lung function of children and young adolescents with SCD in a German single-center cohort and to extend conventional lung function testing by the use of a new imaging method. We performed spirometry and body plethysmography in 35 children and young adults with hemoglobin SS, SC, S/ß-thalassemia as well as 50 controls. These data were compared with clinical characteristics and typical laboratory parameters of hemolysis and disease activity in SCD. To identify lung inhomogeneities, for example due to atelectasis, hyperinflation, air trapping or vascular occlusions, we used the promising new method of electrical impedance tomography (EIT) and calculated global inhomogeneity indices. Results: Lung function of patients with SCD was significantly reduced compared to that of healthy controls. When the result was found to be pathological, the most commonly observed type of breathing disorder was classified as restrictive. Laboratory parameters showed typical features of SCD including decreased levels of hemoglobin and hematocrit and elevated levels of leucocytes, platelets, lactate dehydrogenase and total bilirubin. However, there was no correlation between blood values and reduced lung function. Electrical impedance tomography (EIT) revealed no abnormalities in SCD patients compared to healthy controls. In particular, we were unable to demonstrate any regional inhomogeneities in lung ventilation. Conclusion: In our study, SCD patients showed impaired lung function, with a relevant percentage of patients suffering from restrictive breathing disorder. Signs of obstruction could not be detected. Electrical impedance tomography (EIT) measurements revealed no unevenness that would suggest air entrapment, blockage of blood vessels, excessive inflation, obstruction, or other forms of lung disease. Additionally, the reduction in lung function observed in SCD patients was not related to the disease severity or laboratory test results.

Spatiotemporal gait parameters in young individuals wearing an age simulation suit compared to healthy older individuals.

INTRODUCTION: Aging is accompanied by changes in muscle mass, strength and loss of sensory, visual and auditive functions. However, these changes do not occur linearly, most spatiotemporal gait parameters change with aging. Age simulation suits have been invented to give young people an impression of the implications of being older and may be a useful tool in the scientific setting for gerontology research to validate any study concept before it becomes a pilot study. The rationale behind this study was to investigate the effects of an age simulation suit on gait parameters in young healthy adults and to compare the altered gait with healthy older, community-dwelling citizens. METHODS: Subjects were 14 healthy young adults (6 female) and 8 healthy older (4 female) individuals with a mean (± SD) age of 24.8 ± 3.4 years and 72 ± 1.9 years, respectively. After initial baseline measurements had been taken and a familiarization phase, the younger subjects walked for 15 min without and 15 min with an age simulation suit on an instrumented treadmill. The older subjects walked once for 15 min on the same treadmill without wearing an age simulation suit. The walking speed was self-selected for all subjects. RESULTS: The age simulation suit reduced the walking speed from 4.1 ± 0.7 km/h to 3.3 ± 0.5 km/h (p < 0.001) in young adults with no differences compared to older adults (2.9 ± 0.6 km/h, p = 0.9). Step width increased from 8.7 ± 2.2 cm to 12.1 ± 2.2 cm (p < 0.001) and did not differ from older participants (11.1 ± 4.3 cm, p = 0.37). The stride length was reduced (132.6 ± 5.9 cm vs 118.1 +-6.6 cm, p < 0.001), but still did not match the old control group (94.5 ± 5.6 cm, p < 0.05). Wearing the suit increased thestride time of young subjects (from 1,152 to 1,316 ms, p < 0.001) and was different compared to the older control group (1,172 ms, p = 0.53). The coefficient of variation (COV) of spatiotemporal parameters did not differ between young (both not wearing the suit and wearing the suit) and older subjects. The standard deviation of lateral symmetry, an in-house marker from the instrumented treadmill that serves as a marker of gait variability, differed between young subjects without the suit and older subjects (5.89 ± 1.9 mm vs 14.6 ± 5.7 mm, p < 0.001) but not between young subjects wearing the suit and older subjects (16.4 ± 7.4 mm vs 14.6 ± 5.7 mm, p = 0.53). CONCLUSION: Wearing an age simulation suit while walking on a treadmill with a self-selected walking speed alters some, but not all, measured spatiotemporal parameters to approximate a gait pattern similar to that of an older individual.

Identification of Individually Altered Gait Behavior Using an Unobtrusive IMU Sensor Setup.

Gait behavior is considered an important indicator for the assessment of the general health status and provides a diagnostic observation for neuro-degenerative and musculo-skeletal diseases. Individual changes in gait behavior often reflect a deterioration of the current health status in a general sense and therefore provide significant information for clinicians and care-givers. In this work, we have used an unobtrusive sensor setup comprising three inertial measurement units (IMUs) located at the wrist, the chest and the thigh to obtain an objective measure of the human locomotion. We conducted a clinical trial in a movement laboratory environment to obtain a database of gait data at different walking speeds and conditions. The aging-simulation suit GERT was used to deteriorate the individual gait behavior during the experiments. Treadmill walking trials were used to train different classifiers to discriminate normal walking from GERT-affected walking patterns. Level-ground walking trials were used to validate the previously generated classifiers. A five-fold cross validation during the training process yielded overall F1-scores between 0.965 and 0.986. The validation tests showed promising results with prediction accuracies of more than 80%. Clinical relevance- The clinical relevance of this contri-bution can be considered two-fold. First we demonstrate the possibility of an unobtrusive monitoring system to iden-tify individual deterioration of gait behavior. Second we also validate the use of aging-simulation suits to introduce individual changes of gait patterns in healthy subjects to create a database of simulated yet realistic gait impairments associated with aging.

Step Length Estimation with Wearable Wrist Sensor using ANN.

Step Length is an important metric that can be used for the analysis and assessment of the gait. Proper dynamical models are not available in current literature associated with the wrist that can adequately determine the step length using recursive estimation techniques. This study presents a method to estimate the step length using angular velocity data from the wrist sensor. The technique maps the dynamical region corresponding to periods of activity of the gait manifested in angular velocity from the inertial measurement unit located at the wrist to that of the thigh using an artificial neural network, upon which an unscented Kalman filter is used to determine the horizontal position of the foot relative to the hip, and consequently, determine step length. The results for Step Length indicate an average accuracy of 81.8% and 91.1% for the young and elderly, respectively, when compared to a reference system, which, in our study, is data from a treadmill.

Using Synthesized IMU Data to Train a Long-Short Term Memory-based Neural Network for Unobtrusive Gait Analysis with a Sparse Sensor Setup.

In this work, we evaluated the possibility to use synthesized IMU data for training a deep neural network to generate a more complex, full-body description of the human gait in terms of joint angle trajectories from a sparse sensor setup. In this context, a sparse sensor setup consists of a few sensors attached to human body segments in an unobtrusive manner to possibly provide a monitoring system in an everyday life scenario. Since the relation between the input IMU data and the output joint angle trajectories is highly non-linear, neural networks appear to provide an optimal framework to formulate a mapping description. Especially with respect to periodic signals, recurrent neural networks (RNNs) have gained importance in the recent years. In this work, we have used a special type of RNNs that can be implemented by using long-short term memory (LSTM) cells, which have shown promising results when being applied to sequential data. The artificial training data was generated by a simulative human gait model and virtually attached sensor devices. The trained network was subsequently validated by a dataset that was recorded from a treadmill walking trial using a motion capturing system and an IMU sensor system. The qualitative comparison already shows promising results, however, this study can only be considered to provide preliminary results in this area. Clinical Relevance- This approach has the potential to be applied in the remote assessment of gait behavior during everyday life environments using an unobtrusive sensor net-work. In particular for monitoring older people suffering from an increased fall risk or any significant gait impairments this work is of possible interest.

Estimation of Step Length With Wearable Thigh Sensor Using an Unscented Kalman Filter.

The determination of step length, an important gait parameter, has been a challenging task. Although unobtrusive sensors (inertial measurement units) have been developed recently, they cannot facilitate the automatic estimation of step length. In this article, we use a model-based technique to determine the step length using the Unscented Kalman Filter with angular velocity from a gyroscope inside the thigh pocket. We then propose a novel covariance estimation algorithm based on a screening technique that performs a search for the optimal Process Noise Covariance matrix. Upon implementing the Unscented Kalman Filter, the step length is found using the horizontal position of the foot relative to the hip using a patient-independent robust peak detection algorithm. This research article paves the way for algorithms that are computationally much faster than black box methods, with more scope for the development of better algorithms for covariance estimation using the one proposed in this article as a foundation.

A Rotational Invariant Neural Network for Electrical Impedance Tomography Imaging without Reference Voltage: RF-REIM-NET.

Background: Electrical Impedance Tomography (EIT) is a radiation-free technique for image reconstruction. However, as the inverse problem of EIT is non-linear and ill-posed, the reconstruction of sharp conductivity images poses a major problem. With the emergence of artificial neural networks (ANN), their application in EIT has recently gained interest. Methodology: We propose an ANN that can solve the inverse problem without the presence of a reference voltage. At the end of the ANN, we reused the dense layers multiple times, considering that the EIT exhibits rotational symmetries in a circular domain. To avoid bias in training data, the conductivity range used in the simulations was greater than expected in measurements. We also propose a new method that creates new data samples from existing training data. Results: We show that our ANN is more robust with respect to noise compared with the analytical Gauss-Newton approach. The reconstruction results for EIT phantom tank measurements are also clearer, as ringing artefacts are less pronounced. To evaluate the performance of the ANN under real-world conditions, we perform reconstructions on an experimental pig study with computed tomography for comparison. Conclusions: Our proposed ANN can reconstruct EIT images without the need of a reference voltage.

A Wearable, Multi-Frequency Device to Measure Muscle Activity Combining Simultaneous Electromyography and Electrical Impedance Myography.

The detection of muscle contraction and the estimation of muscle force are essential tasks in robot-assisted rehabilitation systems. The most commonly used method to investigate muscle contraction is surface electromyography (EMG), which, however, shows considerable disadvantages in predicting the muscle force, since unpredictable factors may influence the detected force but not necessarily the EMG data. Electrical impedance myography (EIM) investigates the change in electrical impedance during muscle activities and is another promising technique to investigate muscle functions. This paper introduces the design, development, and evaluation of a device that performs EMG and EIM simultaneously for more robust measurement of muscle conditions subject to artifacts. The device is light, wearable, and wireless and has a modular design, in which the EMG, EIM, micro-controller, and communication modules are stacked and interconnected through connectors. As a result, the EIM module measures the bioimpedance between 20 and 200 Ω with an error of less than 5% at 140 SPS. The settling time during the calibration phase of this module is less than 1000 ms. The EMG module captures the spectrum of the EMG signal between 20-150 Hz at 1 kSPS with an SNR of 67 dB. The micro-controller and communication module builds an ARM-Cortex M3 micro-controller which reads and transfers the captured data every 1 ms over RF (868 Mhz) with a baud rate of 500 kbps to a receptor connected to a PC. Preliminary measurements on a volunteer during leg extension, walking, and sit-to-stand showed the potential of the system to investigate muscle function by combining simultaneous EMG and EIM.

Model-Based Step Length Estimation Using a Pendant-Integrated Mobility Sensor.

The step length is an important parameter in gait analysis. Long-term monitoring applications for gait analysis are often based on inertial measurement units (IMUs) due to their low-cost and unobtrusive nature. Spatial gait parameters, such as step or stride length, are therefore not directly accessible. In this contribution, we focus on model-based algorithms for step length estimation based on a pendant-integrated IMU during slow walking speeds. We present a model-based approach to estimate the step length, which is divided into two successive steps. As the first part of our approach, we present an algorithm for estimation of the vertical displacement of the center of mass (CoM) during gait. Based on this estimate, we present a novel approach to estimate the step length, which we have deduced from a previously published, simplified gait model. The algorithm is compared to a commonly known approach for accelometry-based step length prediction and validated against reference data obtained from a force plate-integrated treadmill for gait analysis during a clinical study with ten healthy subjects. Due to the applicability to gait stability assessment in elderly or gait impaired patients, we focus on slow walking speeds (1-4 km h-1). The presented algorithms outperform the existing approach and the proposed model calculations provide a more accurate prediction. For the vertical displacement, we achieved a precision of 9.3% (CoV) with an RMSE of 1.5 mm in terms of the trajectory amplitude during normal gait patterns. The step length estimation yields satisfying results with a relative prediction error of lower than 10% for walking speeds of 2-4kmh-1.

Determination of the Geometric Parameters of Electrode Systems for Electrical Impedance Myography: A Preliminary Study.

The electrical impedance myography method is widely used in solving bionic control problems and consists of assessing the change in the electrical impedance magnitude during muscle contraction in real time. However, the choice of electrode systems sizes is not always properly considered when using the electrical impedance myography method in the existing approaches, which is important in terms of electrical impedance signal expressiveness and reproducibility. The article is devoted to the determination of acceptable sizes for the electrode systems for electrical impedance myography using the Pareto optimality assessment method and the electrical impedance signals formation model of the forearm area, taking into account the change in the electrophysical and geometric parameters of the skin and fat layer and muscle groups when performing actions with a hand. Numerical finite element simulation using anthropometric models of the forearm obtained by volunteers' MRI 3D reconstructions was performed to determine a sufficient degree of the forearm anatomical features detailing in terms of the measured electrical impedance. For the mathematical description of electrical impedance relationships, a forearm two-layer model, represented by the skin-fat layer and muscles, was reasonably chosen, which adequately describes the change in electrical impedance when performing hand actions. Using this model, for the first time, an approach that can be used to determine the acceptable sizes of electrode systems for different parts of the body individually was proposed.

A Way of Bionic Control Based on EI, EMG, and FMG Signals.

Creating highly functional prosthetic, orthotic, and rehabilitation devices is a socially relevant scientific and engineering task. Currently, certain constraints hamper the development of such devices. The primary constraint is the lack of an intuitive and reliable control interface working between the organism and the actuator. The critical point in developing these devices and systems is determining the type and parameters of movements based on control signals recorded on an extremity. In the study, we investigate the simultaneous acquisition of electric impedance (EI), electromyography (EMG), and force myography (FMG) signals during basic wrist movements: grasping, flexion/extension, and rotation. For investigation, a laboratory instrumentation and software test setup were made for registering signals and collecting data. The analysis of the acquired signals revealed that the EI signals in conjunction with the analysis of EMG and FMG signals could potentially be highly informative in anthropomorphic control systems. The study results confirm that the comprehensive real-time analysis of EI, EMG, and FMG signals potentially allows implementing the method of anthropomorphic and proportional control with an acceptable delay.

Estimation of Stride Time Variability in Unobtrusive Long-Term Monitoring Using Inertial Measurement Sensors.

Stride time variability is an important indicator for the assessment of gait stability. An accurate extraction of the stride intervals is essential for determining stride time variability. Peak detection is a commonly used method for gait segmentation and stride time estimation. Standard peak detection algorithms often fail due to additional movement components and measurement noise. A novel algorithm for robust peak detection in inertial sensor signals was proposed in a previous contribution. In this work, we present a novel approach for estimation of stride time variability based on the formerly proposed peak detection algorithm applied to an unobtrusive sensor setup for motion monitoring. The unobtrusive sensor setup includes a wrist sensor, a pocket or belt sensor, and a necklace sensor, all equipped with both accelerometer and gyroscope. The goal of this work is to implement a generalized approach for accurate and robust stride interval determining algorithm for different sensor locations. Therefore, treadmill and level ground walking experiments were conducted with ten healthy subjects at increasing walking speeds and an age-simulating suit. With the proposed algorithm, we achieved a RMSE of 0.07 s for the stride interval estimation during treadmill walking experiments. The results give promising indications that detection of variation of stride time variability is possible using the proposed unobtrusive sensor setup.

Evaluation and Application of a Customizable Wireless Platform: A Body Sensor Network for Unobtrusive Gait Analysis in Everyday Life.

Body sensor networks (BSNs) represent an important research tool for exploring novel diagnostic or therapeutic approaches. They allow for integrating different measurement techniques into body-worn sensors organized in a network structure. In 2011, the first Integrated Posture and Activity Network by MedIT Aachen (IPANEMA) was introduced. In this work, we present a recently developed platform for a wireless body sensor network with customizable applications based on a proprietary 868MHz communication interface. In particular, we present a sensor setup for gait analysis during everyday life monitoring. The arrangement consists of three identical inertial measurement sensors attached at the wrist, thigh, and chest. We additionally introduce a force-sensitive resistor integrated insole for measurement of ground reaction forces (GRFs), to enhance the assessment possibilities and generate ground truth data for inertial measurement sensors. Since the 868MHz is not strongly represented in existing BSN implementations, we validate the proposed system concerning an application in gait analysis and use this as a representative demonstration of realizability. Hence, there are three key aspects of this project. The system is evaluated with respect to (I) accurate timing, (II) received signal quality, and (III) measurement capabilities of the insole pressure nodes. In addition to the demonstration of feasibility, we achieved promising results regarding the extractions of gait parameters (stride detection accuracy: 99.6±0.8%, Root-Mean-Square Deviation (RMSE) of mean stride time: 5ms, RMSE of percentage stance time: 2.3%). Conclusion: With the satisfactory technical performance in laboratory and application environment and the convincing accuracy of the gait parameter extraction, the presented system offers a solid basis for a gait monitoring system in everyday life.

Object-oriented modeling of thoracic fluid balance to study cardiogenic pulmonary congestion in humans.

BACKGROUND AND OBJECTIVE: We hypothesized that a biophysical computational model implemented in an object-oriented modeling language (OOML) would provide physiological information and simulative data to study the development and treatment of cardiogenic pulmonary congestion. METHODS: This work is based on the object-oriented cardiopulmonary interaction introduced in [1]. This paper describes the novel model components required to study cardiogenic pulmonary congestion: i) interstitial fluid exchange related to the Starling equation, ii) the lymphatic pump, and iii) the interconnection of these elements with the original cardiopulmonary model. The presented model succeeds in i) describing lymphatic flow at the capillary artery and venous end, ii) activation of the lymphatic pump at elevated pulmonary pressures, and iii) the simulation of the different safety factors related to lung tissue, osmotic gradient, and the lymphatic system during the development of lung congestion. RESULTS: Simulations show a qualitative correlation between model behavior and physiological data from literature. The model also demonstrates the beneficial effect of continuous positive airway pressure therapy on fluid clearance and respiratory mechanics. CONCLUSION: This study demonstrates the successful use of OOML to describe the development of cardiogenic congestion by introducing a model of the lymphatic system and the thoracic fluid balance system, as well as connecting them to the existing cardiopulmonary model.

An object-oriented computational model to study cardiopulmonary hemodynamic interactions in humans.

BACKGROUND AND OBJECTIVE: This work introduces an object-oriented computational model to study cardiopulmonary interactions in humans. METHODS: Modeling was performed in object-oriented programing language Matlab Simscape, where model components are connected with each other through physical connections. Constitutive and phenomenological equations of model elements are implemented based on their non-linear pressure-volume or pressure-flow relationship. The model includes more than 30 physiological compartments, which belong either to the cardiovascular or respiratory system. The model considers non-linear behaviors of veins, pulmonary capillaries, collapsible airways, alveoli, and the chest wall. Model parameters were derisved based on literature values. Model validation was performed by comparing simulation results with clinical and animal data reported in literature. RESULTS: The model is able to provide quantitative values of alveolar, pleural, interstitial, aortic and ventricular pressures, as well as heart and lung volumes during spontaneous breathing and mechanical ventilation. Results of baseline simulation demonstrate the consistency of the assigned parameters. Simulation results during mechanical ventilation with PEEP trials can be directly compared with animal and clinical data given in literature. CONCLUSIONS: Object-oriented programming languages can be used to model interconnected systems including model non-linearities. The model provides a useful tool to investigate cardiopulmonary activity during spontaneous breathing and mechanical ventilation.

Flow-volume loops measured with electrical impedance tomography in pediatric patients with asthma.

BACKGROUND: Electrical impedance tomography (EIT) provides information on global and regional ventilation during tidal breathing and mechanical ventilation. During forced expiration maneuvers, the linearity of EIT and spirometric data has been documented in healthy persons. The present study investigates the potential diagnostic use of EIT in pediatric patients with asthma. METHODS: EIT and spirometry were performed in 58 children with asthma (average age ± SD: 11.86 ± 3.13 years), and 58 healthy controls (average age ± SD: 12.12 ± 2.9 years). The correlation between EIT data and simultaneously acquired spirometric data were tested for FEV1, FEV0.5 , MEF75 , MEF50 , and MEF25 . Binary classification tests were performed for the EIT-derived Tiffeneau index FEV1 /FVC and the bronchodilator test index ΔFEV1 . Average flow-volume (FV) loops were generated for patients with pathologic spirometry to demonstrate the feasibility of EIT for graphic diagnosis of asthma. RESULTS: Spirometry and global EIT-based FV loops showed a strong correlation (P < 0.001, r > 0.9 in FEV1 and FEV0.5 ). In all criteria, the binary classification tests yielded high specificity (>93%), a high positive predictive value (≥75%) and a high negative predictive value (>80%), while sensitivity was higher in ΔFEV1 (86.67%) and lower in FEV1 /FVC (25% and 35.29%). A typical concave shape of the EIT-derived average FV loops was observed for asthmatic children with improvement after bronchospasmolysis. CONCLUSIONS: Global FV loops derived from EIT correlate well with spirometry. Positive bronchospasmolysis can be observed in EIT-derived FV loops. Flow-volume loops originated from EIT have a potential to visualize pulmonary function.

Electrical impedance tomography as possible guidance for individual positioning of patients with multiple lung injury.

INTRODUCTION: Electrical Impedance Tomography (EIT) is a tomographic, radiation-free technique based on the injection of a harmless alternating current. OBJECTIVE: As electrical impedance strictly correlates with the variation of air content, EIT delivers highly dynamic information about global and regional ventilation. We want to demonstrate the potential of EIT individualizing ventilation by positioning. METHODS: Gravity-dependent EIT findings were analyzed retrospectively in a critically ill mechanically ventilated pediatric patient with cystic fibrosis and coincident lung diseases. To further evaluate gravity-dependent changes in ventilation, six adult healthy and spontaneously breathing volunteers were investigated during simultaneous detection of EIT, breathing patterns, tidal volume (VT) and breathing frequency (BF). RESULTS: EIT findings in healthy lungs in five positions showed gravity-dependent effects of ventilation with overall ventilation of predominantly the right lung (except during left-side positioning) and with the ventral lung in supine, prone and upright position. These EIT-derived observations are in line with pathophysiological mechanisms and earlier EIT studies. Unexpectedly, the patient with cystic fibrosis and lobectomy of the right upper and middle lobe one year earlier, showed improvement of global and regional ventilation in the right position despite reduced lung volume and overinflation of this side. This resulted in individualized positioning and improvement of ventilation. CONCLUSIONS: Although therapeutic recommendations are available for gravitational influences of lung ventilation, they can be contradictory depending on the underlying lung disease. EIT has the potential to guide therapists in the positioning of patients according to their individual condition and disease, especially in case of multiple lung injury.

Assessing regional lung mechanics by combining electrical impedance tomography and forced oscillation technique.

There is a lack of noninvasive pulmonary function tests which can assess regional information of the lungs. Electrical impedance tomography (EIT) is a radiation-free, non-invasive real-time imaging that provides regional information of ventilation volume regarding the measurement of electrical impedance distribution. Forced oscillation technique (FOT) is a pulmonary function test which is based on the measurement of respiratory mechanical impedance over a frequency range. In this article, we introduce a new measurement approach by combining FOT and EIT, named the oscillatory electrical impedance tomography (oEIT). Our oEIT measurement system consists of a valve-based FOT device, an EIT device, pressure and flow sensors, and a computer fusing the data streams. Measurements were performed on five healthy volunteers at the frequencies 3, 4, 5, 6, 7, 8, 10, 15, and 20 Hz. The measurements suggest that the combination of FOT and EIT is a promising approach. High frequency responses are visible in the derivative of the global impedance index ΔZeit(t,fos). $\Delta {Z_{{\text{eit}}}}(t,{f_{{\text{os}}}}).$ The oEIT signals consist of three main components: forced oscillation, spontaneous breathing, and heart activity. The amplitude of the oscillation component decreases with increasing frequency. The band-pass filtered oEIT signal might be a new tool in regional lung function diagnostics, since local responses to high frequency perturbation could be distinguished between different lung regions.